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体验新版 GitCode,发现更多精彩内容 >>
提交
a3123e21
编写于
6月 08, 2017
作者:
C
Cao Ying
提交者:
GitHub
6月 08, 2017
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Merge pull request #2412 from lcy-seso/add_config_helper_for_prelu
add configuration helper for prelu layer.
上级
f703b5b4
99661481
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
212 addition
and
82 deletion
+212
-82
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+14
-5
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+3
-3
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+151
-73
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+2
-1
python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
..._helpers/tests/configs/protostr/test_prelu_layer.protostr
+36
-0
python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
.../trainer_config_helpers/tests/configs/test_prelu_layer.py
+6
-0
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
a3123e21
...
...
@@ -130,7 +130,7 @@ recurrent_group
---------------
.. autoclass:: paddle.v2.layer.recurrent_group
:noindex:
lstm_step
---------
.. autoclass:: paddle.v2.layer.lstm_step
...
...
@@ -145,12 +145,12 @@ beam_search
------------
.. autoclass:: paddle.v2.layer.beam_search
:noindex:
get_output
----------
.. autoclass:: paddle.v2.layer.get_output
:noindex:
Mixed Layer
===========
...
...
@@ -203,7 +203,7 @@ trans_full_matrix_projection
----------------------------
.. autoclass:: paddle.v2.layer.trans_full_matrix_projection
:noindex:
Aggregate Layers
================
...
...
@@ -434,10 +434,19 @@ smooth_l1_cost
.. autoclass:: paddle.v2.layer.smooth_l1_cost
:noindex:
Check Layer
Check Layer
============
eos
---
.. autoclass:: paddle.v2.layer.eos
:noindex:
Activation with learnable parameter
===================================
prelu
--------
.. autoclass:: paddle.v2.layer.prelu
:noindex:
python/paddle/trainer/config_parser.py
浏览文件 @
a3123e21
...
...
@@ -73,7 +73,6 @@ To use this from paddle_trainer, paddle_trainer should be called with
--config_args=extension_module_name=[MODULE_NAME]
'''
import
copy
import
logging
import
os
...
...
@@ -1731,9 +1730,10 @@ class ParameterReluLayer(LayerBase):
def
__init__
(
self
,
name
,
inputs
,
partial_sum
=
1
,
**
args
):
super
(
ParameterReluLayer
,
self
).
__init__
(
name
,
self
.
layer_type
,
0
,
inputs
=
inputs
,
**
args
)
config_assert
(
len
(
self
.
inputs
)
==
1
)
config_assert
(
self
.
input_layer
.
size
%
partial_sum
==
0
)
input_layer
=
self
.
get_input_layer
(
0
)
config_assert
(
len
(
self
.
inputs
)
==
1
,
"prelu layer has only one input."
)
config_assert
(
input_layer
.
size
%
partial_sum
==
0
,
"a wrong setting for partial_sum"
)
self
.
set_layer_size
(
input_layer
.
size
)
self
.
create_input_parameter
(
0
,
input_layer
.
size
/
partial_sum
)
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
a3123e21
...
...
@@ -31,31 +31,31 @@ except ImportError:
import
copy
__all__
=
[
"full_matrix_projection"
,
"AggregateLevel"
,
"ExpandLevel"
,
"identity_projection"
,
"dotmul_projection"
,
"dotmul_operator"
,
"repeat_layer"
,
"seq_reshape_layer"
,
"table_projection"
,
"mixed_layer"
,
"data_layer"
,
"embedding_layer"
,
"fc_layer"
,
"grumemory"
,
"pooling_layer"
,
"lstmemory"
,
"last_seq"
,
"first_seq"
,
"cos_sim"
,
"hsigmoid"
,
"conv_projection"
,
"mse_cost"
,
"regression_cost"
,
'full_matrix_projection'
,
'AggregateLevel'
,
'ExpandLevel'
,
'identity_projection'
,
'dotmul_projection'
,
'dotmul_operator'
,
'repeat_layer'
,
'seq_reshape_layer'
,
'table_projection'
,
'mixed_layer'
,
'data_layer'
,
'embedding_layer'
,
'fc_layer'
,
'grumemory'
,
'pooling_layer'
,
'lstmemory'
,
'last_seq'
,
'first_seq'
,
'cos_sim'
,
'hsigmoid'
,
'conv_projection'
,
'mse_cost'
,
'regression_cost'
,
'classification_cost'
,
"LayerOutput"
,
'LayerOutput'
,
'img_conv_layer'
,
'img_pool_layer'
,
'batch_norm_layer'
,
...
...
@@ -121,6 +121,7 @@ __all__ = [
'smooth_l1_cost'
,
'layer_support'
,
'multiplex_layer'
,
'prelu_layer'
,
]
...
...
@@ -129,26 +130,26 @@ class LayerType(object):
Layer type enumerations.
"""
DATA
=
"data"
MIXED_LAYER
=
"mixed"
LSTMEMORY
=
"lstmemory"
GRUMEMORY
=
"gated_recurrent"
SEQUENCE_LAST_INSTANCE
=
"seqlastins"
SEQUENCE_FIRST_INSTANCE
=
"seqfirstins"
SEQUENCE_RESHAPE
=
"seqreshape"
POOLING_MAX
=
"max"
DATA
=
'data'
MIXED_LAYER
=
'mixed'
LSTMEMORY
=
'lstmemory'
GRUMEMORY
=
'gated_recurrent'
SEQUENCE_LAST_INSTANCE
=
'seqlastins'
SEQUENCE_FIRST_INSTANCE
=
'seqfirstins'
SEQUENCE_RESHAPE
=
'seqreshape'
POOLING_MAX
=
'max'
POOLING_AVG
=
'average'
FC_LAYER
=
"fc"
FC_LAYER
=
'fc'
COST
=
'cost'
COSINE_SIM_VEC
=
'cos_vm'
COSINE_SIM
=
'cos'
HSIGMOID
=
'hsigmoid'
CONV_LAYER
=
"conv"
CONVTRANS_LAYER
=
"convt"
EXCONV_LAYER
=
"exconv"
EXCONVTRANS_LAYER
=
"exconvt"
CUDNNCONV_LAYER
=
"cudnn_conv"
POOL_LAYER
=
"pool"
CONV_LAYER
=
'conv'
CONVTRANS_LAYER
=
'convt'
EXCONV_LAYER
=
'exconv'
EXCONVTRANS_LAYER
=
'exconvt'
CUDNNCONV_LAYER
=
'cudnn_conv'
POOL_LAYER
=
'pool'
BATCH_NORM_LAYER
=
'batch_norm'
NORM_LAYER
=
'norm'
SUM_TO_ONE_NORM_LAYER
=
'sum_to_one_norm'
...
...
@@ -177,36 +178,38 @@ class LayerType(object):
EOSID_LAYER
=
'eos_id'
RECURRENT_LAYER
=
'recurrent'
CONV_SHIFT_LAYER
=
"conv_shift"
TENSOR_LAYER
=
"tensor"
SEL_FC_LAYER
=
"selective_fc"
SAMPLING_ID_LAYER
=
"sampling_id"
SLOPE_INTERCEPT_LAYER
=
"slope_intercept"
LINEAR_COMBINATION_LAYER
=
"convex_comb"
BLOCK_EXPAND
=
"blockexpand"
MAXOUT
=
"maxout"
SPP_LAYER
=
"spp"
PAD_LAYER
=
"pad"
MULTIPLEX_LAYER
=
"multiplex"
PRINT_LAYER
=
"print"
PRIORBOX_LAYER
=
"priorbox"
CTC_LAYER
=
"ctc"
WARP_CTC_LAYER
=
"warp_ctc"
CRF_LAYER
=
"crf"
CRF_DECODING_LAYER
=
"crf_decoding"
CONV_SHIFT_LAYER
=
'conv_shift'
TENSOR_LAYER
=
'tensor'
SEL_FC_LAYER
=
'selective_fc'
SAMPLING_ID_LAYER
=
'sampling_id'
SLOPE_INTERCEPT_LAYER
=
'slope_intercept'
LINEAR_COMBINATION_LAYER
=
'convex_comb'
BLOCK_EXPAND
=
'blockexpand'
MAXOUT
=
'maxout'
SPP_LAYER
=
'spp'
PAD_LAYER
=
'pad'
MULTIPLEX_LAYER
=
'multiplex'
PRINT_LAYER
=
'print'
PRIORBOX_LAYER
=
'priorbox'
CTC_LAYER
=
'ctc'
WARP_CTC_LAYER
=
'warp_ctc'
CRF_LAYER
=
'crf'
CRF_DECODING_LAYER
=
'crf_decoding'
NCE_LAYER
=
'nce'
RANK_COST
=
"rank-cost"
LAMBDA_COST
=
"lambda_cost"
HUBER
=
"huber"
CROSS_ENTROPY
=
"multi-class-cross-entropy"
CROSS_ENTROPY_WITH_SELFNORM
=
"multi_class_cross_entropy_with_selfnorm"
SOFT_BIN_CLASS_CROSS_ENTROPY
=
"soft_binary_class_cross_entropy"
MULTI_BIN_LABEL_CROSS_ENTROPY
=
"multi_binary_label_cross_entropy"
SUM_COST
=
"sum_cost"
SMOOTH_L1
=
"smooth_l1"
RANK_COST
=
'rank-cost'
LAMBDA_COST
=
'lambda_cost'
HUBER
=
'huber'
CROSS_ENTROPY
=
'multi-class-cross-entropy'
CROSS_ENTROPY_WITH_SELFNORM
=
'multi_class_cross_entropy_with_selfnorm'
SOFT_BIN_CLASS_CROSS_ENTROPY
=
'soft_binary_class_cross_entropy'
MULTI_BIN_LABEL_CROSS_ENTROPY
=
'multi_binary_label_cross_entropy'
SUM_COST
=
'sum_cost'
SMOOTH_L1
=
'smooth_l1'
PRELU
=
'prelu'
@
staticmethod
def
is_layer_type
(
type_name
):
...
...
@@ -4722,7 +4725,7 @@ def ctc_layer(input,
fc_layer with softmax activation, should be num_classes + 1. The size of ctc_layer
should also be num_classes + 1.
The
simple usage
:
The
example usage is
:
.. code-block:: python
...
...
@@ -4809,7 +4812,7 @@ def warp_ctc_layer(input,
- As a native 'softmax' activation is interated to the warp-ctc library,
'linear' activation is expected instead in the 'input' layer.
The
simple usage
:
The
example usage is
:
.. code-block:: python
...
...
@@ -4870,7 +4873,7 @@ def crf_layer(input,
A layer for calculating the cost of sequential conditional random
field model.
The
simple usage
:
The
example usage is
:
.. code-block:: python
...
...
@@ -4944,7 +4947,7 @@ def crf_decoding_layer(input,
this layer will also calculate error. output.value[i] is 1 for incorrect
decoding or 0 for correct decoding.
The
simple usage
:
The
example usage is
:
.. code-block:: python
...
...
@@ -5137,7 +5140,7 @@ def rank_cost(left,
- :math:`o_i` and :math:`o_j`: the left output and right output.
Their dimension is one.
The
simple usage
:
The
example usage is
:
.. code-block:: python
...
...
@@ -5194,7 +5197,7 @@ def lambda_cost(input,
"""
lambdaCost for lambdaRank LTR approach.
The
simple usage
:
The
example usage is
:
.. code-block:: python
...
...
@@ -5252,6 +5255,8 @@ def cross_entropy(input,
"""
A loss layer for multi class entropy.
The example usage is:
.. code-block:: python
cost = cross_entropy(input=input_layer,
...
...
@@ -5298,6 +5303,8 @@ def cross_entropy_with_selfnorm(input,
A loss layer for multi class entropy with selfnorm.
Input should be a vector of positive numbers, without normalization.
The example usage is:
.. code-block:: python
cost = cross_entropy_with_selfnorm(input=input_layer,
...
...
@@ -5339,6 +5346,8 @@ def sum_cost(input, name=None, layer_attr=None):
"""
A loss layer which calculate the sum of the input as loss
The example usage is:
.. code-block:: python
cost = sum_cost(input=input_layer)
...
...
@@ -5368,6 +5377,8 @@ def huber_cost(input, label, name=None, coeff=1.0, layer_attr=None):
"""
A loss layer for huber loss.
The example usage is:
.. code-block:: python
cost = huber_cost(input=input_layer,
...
...
@@ -5408,6 +5419,8 @@ def multi_binary_label_cross_entropy(input,
"""
A loss layer for multi binary label cross entropy.
The example usage is:
.. code-block:: python
cost = multi_binary_label_cross_entropy(input=input_layer,
...
...
@@ -5467,6 +5480,8 @@ def smooth_l1_cost(input, label, name=None, coeff=1.0, layer_attr=None):
More details can be found by referring to `Fast R-CNN
<https://arxiv.org/pdf/1504.08083v2.pdf>`_
The example usage is:
.. code-block:: python
cost = smooth_l1_cost(input=input_layer,
...
...
@@ -5516,6 +5531,8 @@ def multiplex_layer(input, name=None, layer_attr=None):
where, y is output. :math:`x_{k}` is the k-th input layer and
:math:`k = x_{0}[i] + 1`.
The example usage is:
.. code-block:: python
maxid = multiplex_layer(input=layers)
...
...
@@ -5548,3 +5565,64 @@ def multiplex_layer(input, name=None, layer_attr=None):
layer_type
=
LayerType
.
MULTIPLEX_LAYER
,
parents
=
input
,
size
=
l
.
config
.
size
)
@
wrap_name_default
()
@
layer_support
()
@
wrap_name_default
()
@
wrap_param_attr_default
()
def
prelu_layer
(
input
,
name
=
None
,
partial_sum
=
1
,
param_attr
=
None
,
layer_attr
=
None
):
"""
The Parameter Relu activation that actives outputs with a learnable weight.
Reference:
Delving Deep into Rectifiers: Surpassing Human-Level Performance on
ImageNet Classification http://arxiv.org/pdf/1502.01852v1.pdf
.. math::
z_i &
\\
quad if
\\
quad z_i > 0
\\\\
a_i * z_i &
\\
quad
\\
mathrm{otherwise}
The example usage is:
.. code-block:: python
prelu = prelu_layer(input=layers, partial_sum=1)
:param name: Name of this layer.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput
:param partial_sum: this parameter makes a group of inputs share a same weight.
- partial_sum = 1, indicates the element-wise activation: each element has a weight.
- partial_sum = number of elements in one channel, indicates the channel-wise activation, elements in a channel share a same weight.
- partial_sum = number of outputs, indicates all elements share a same weight.
:type partial_sum: int
:param param_attr: The parameter attribute. See ParameterAttribute for details.
:type param_attr: ParameterAttribute|None
:param layer_attr: Extra layer configurations. Default is None.
:type layer_attr: ExtraLayerAttribute|None
:return: LayerOutput object.
:rtype: LayerOutput
"""
assert
isinstance
(
input
,
LayerOutput
),
'prelu_layer only accepts one input'
assert
isinstance
(
param_attr
,
ParameterAttribute
)
l
=
Layer
(
name
=
name
,
type
=
LayerType
.
PRELU
,
inputs
=
Input
(
input
.
name
,
**
param_attr
.
attr
),
partial_sum
=
partial_sum
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
))
return
LayerOutput
(
name
=
name
,
layer_type
=
LayerType
.
PRELU
,
parents
=
input
,
size
=
l
.
config
.
size
)
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
a3123e21
...
...
@@ -5,6 +5,7 @@ last_first_seq test_expand_layer test_ntm_layers test_hsigmoid
img_layers img_trans_layers util_layers simple_rnn_layers unused_layers test_cost_layers
test_rnn_group shared_fc shared_lstm shared_gru test_cost_layers_with_weight
test_spp_layer test_bilinear_interp test_maxout test_bi_grumemory math_ops
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
)
test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_prelu_layer.protostr
0 → 100644
浏览文件 @
a3123e21
type: "nn"
layers {
name: "input"
type: "data"
size: 300
active_type: ""
}
layers {
name: "__prelu_layer_0__"
type: "prelu"
size: 300
active_type: ""
inputs {
input_layer_name: "input"
input_parameter_name: "___prelu_layer_0__.w0"
}
}
parameters {
name: "___prelu_layer_0__.w0"
size: 300
initial_mean: 0.0
initial_std: 0.057735026919
initial_strategy: 0
initial_smart: true
}
input_layer_names: "input"
output_layer_names: "__prelu_layer_0__"
sub_models {
name: "root"
layer_names: "input"
layer_names: "__prelu_layer_0__"
input_layer_names: "input"
output_layer_names: "__prelu_layer_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_prelu_layer.py
0 → 100644
浏览文件 @
a3123e21
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'input'
,
size
=
300
)
prelu
=
prelu_layer
(
input
=
data
)
outputs
(
prelu
)
编辑
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